Syllabus for Statistical Machine Learning

Statistisk maskininlärning

A revised version of the syllabus is available.

Syllabus

  • 5 credits
  • Course code: 1RT700
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Technology A1N, Image Analysis and Machine Learning A1N, Mathematics A1N, Computer Science A1N, Data Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2016-03-08
  • Established by: The Faculty Board of Science and Technology
  • Revised: 2017-05-02
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2017
  • Entry requirements:

    120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus and a course in introductory programming.

  • Responsible department: Department of Information Technology

Learning outcomes

Students who pass the course should be able to:

  • Structure and divide statistical learning problems into tractable sub-problems, formulate a mathematical solution to the problems and implement this solution using statistical software.
  • Use and develop linear and nonlinear models for classification and regression.
  • Describe the limitations of linear models and understand how these limitations can be handled using nonlinear models.
  • Explain how the quality of a model can be evaluated and how model selection and tuning can be done using cross validation.
  • Explain the trade-off between bias and variance.
  • Describe the difference between parametric and nonparametric models.

Content

This is an introductory course in statistical machine learning, focusing on classification and regression: linear regression, regularization (ridge regression and LASSO), classification via logistic regression, linear discriminant analysis, classification and regression trees, boosting, neural networks, deep learning; practical considerations such as cross validation, model selection, the bias-variance trade off, applying the methods to real data.

Instruction

Lectures, problem solving sessions (both with and without computer), laboratory work, feedback on written assignments.

Assessment

Written exam combined with oral and written presentation of assignments.

Syllabus Revisions

Reading list

Reading list

Applies from: Autumn 2017

Some titles may be available electronically through the University library.

  • An introduction to statistical learning : with applications in R James, Gareth; James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert

    New York, NY: Springer, 2013

    Find in the library

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